(MOD) Metrics for Capturing Crucial Social Dynamics of Innovative Regions: Implications for S&T Policy

University Of California-San Diego, La Jolla CA

Investigators

Abstract

This research quantifies and documents the role of social dynamics in the economic growth and development of regions with great scientific institutions. The presence of major scientific research institutions in a region does not, by itself, lead to social and economic benefit in that region. The importance of hard assets such as R&D expenditures, patents and venture capital, as well as the critical mass and propinquity of scientific talent is well known. However, this research probes in detail a set of social mechanisms which are postulated to enable some regions to transfer and commercialize knowledge more effectively than others. It examines the importance of the robust activity of S&T boundary-spanning organizations in a region in contributing to successful technology commercialization as measured by number of new business startups. These S&T boundary-spanning organizations, social gatherings of cross-disciplinary and cross-functional groups including both technologists and their business supporters, are of three types: formal groups that support local technologies (trade associations and entrepreneur-aiding groups); volunteer-run chapters affiliated with national organizations (e.g. IEEE, AeA, Sigma Xi, AWIS); and informal volunteer-run affinity groups focused often on a particular interdisciplinary subject such as systems biology or nanotechnology. Intellectual Merit Previous research on social dynamics, largely descriptive and anecdotal, has yet to inform policy. This project studies and quantifies the following aspects of the region's boundary-spanning groups: their types, numbers, meeting frequency and attendance, their membership diversity (Shannon-Wiener index), and the region's overall membership overlap among these boundary-spanning groups. These independent variables are then correlated with new business startups including controls for R&D expenditures, patent activity, venture capital, critical mass and propinquity of the region?s scientific workforce. An initial set of three pilot case studies represent the first step in the development of a predictive model based on machine learning techniques. A recommended data structure is being developed so that other research institutions or their regions may also gather data to perform self assessments and thus add to the training and test data used to test and enhance the resulting model. Broader impacts: This work develops metrics which capture "cultural and social dynamics" and correlates them with successful knowledge flows between research institutions and a dynamic regional commercialization ecology. It searches for the social factors that distinguish vibrant regions with the capacity to innovate from those that do less, in order to better understand the processes by which investments in S&T research are transformed into positive social and economic outcomes. This information may influence how regions and research organizations set policy to stimulate social networks and inclusive interdisciplinary discourse groups. The increased understanding of previously-uncharacterized aspects of knowledge flows should lead directly to new mechanisms available for optimization by policy makers. The application of artificial intelligence machine learning techniques in this work should also lead to new capabilities in the science of science policy.